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arXiv · 2502.20281

Data Jamboree: A Party of Open-Source Software Solving Real-World Data Science Problems

Lucy D'Agostino McGowan·Shannon Tass·Sam Tyner·HaiYing Wang·Jun Yan
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Abstract

The evolving focus in statistics and data science education highlights the growing importance of computing. This paper presents the Data Jamboree, a live event that combines computational methods with traditional statistical techniques to address real-world data science problems. Participants, ranging from novices to experienced users, followed workshop leaders in using open-source tools like Julia, Python, and R to perform tasks such as data cleaning, manipulation, and predictive modeling. The Jamboree showcased the educational benefits of working with open data, providing participants with practical, hands-on experience. We compared the tools in terms of efficiency, flexibility, and statistical power, with Julia excelling in performance, Python in versatility, and R in statistical analysis and visualization. The paper concludes with recommendations for designing similar events to encourage collaborative learning and critical thinking in data science.

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Lucy D'Agostino McGowan, Shannon Tass, Sam Tyner, HaiYing Wang, Jun Yan. 2025-02-27. Data Jamboree: A Party of Open-Source Software Solving Real-World Data Science Problems. https://doi.org/10.51387/25-nejsds79

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